𝗖𝗼𝗿𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲

✔️ Minimum 6–7 years of relevant experience in 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 / 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲, with production-deployed models

✔️ Strong hands-on expertise in 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗦𝗤𝗟 and production-quality ML engineering

✔️ Experience with supervised/unsupervised learning, feature engineering, model selection and evaluation

✔️ Hands-on experience with 𝗦𝗰𝗶𝗸𝗶𝘁-𝗹𝗲𝗮𝗿𝗻, 𝗫𝗚𝗕𝗼𝗼𝘀𝘁, 𝗟𝗶𝗴𝗵𝘁𝗚𝗕𝗠, 𝗣𝗮𝗻𝗱𝗮𝘀 and 𝗡𝘂𝗺𝗣𝘆

✔️ Experience building and productionizing ML pipelines, batch/real-time inference and REST APIs

✔️ Practical AWS experience, preferably 𝗦𝗮𝗴𝗲𝗠𝗮𝗸𝗲𝗿, 𝗦𝟯, 𝗘𝗖𝗥, 𝗘𝗖𝗦/𝗘𝗞𝗦, 𝗟𝗮𝗺𝗯𝗱𝗮 and 𝗖𝗹𝗼𝘂𝗱𝗪𝗮𝘁𝗰𝗵

✔️ Knowledge of model monitoring, drift detection, explainability, versioning and CI/CD

✔️ Banking / Financial Services experience preferred

𝗚𝗼𝗼𝗱 𝘁𝗼 𝗛𝗮𝘃𝗲: 𝗗𝗮𝘁𝗮𝗶𝗸𝘂, 𝗠𝗟𝗳𝗹𝗼𝘄, 𝗗𝗼𝗰𝗸𝗲𝗿, 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝗧𝗲𝗿𝗿𝗮𝗳𝗼𝗿𝗺, 𝗦𝗛𝗔𝗣 and exposure to Generative AI / LLM applications.